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Intraguild processes drive space‐use patterns in a large‐bodied marine predator community. <i>J. Anim. Ecol. 93(7)</i>: 876-890. <a href=\"https://dx.doi.org/10.1111/1365-2656.14108\" target=\"_blank\">https://dx.doi.org/10.1111/1365-2656.14108</a>","AutID":237715,"MonDate":null,"AnaDate":2024,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":365618,"RR":"<b>Morinay, J.; Riotte-Lambert, L.; Aarts, G.M; De Pascalis, F.; Imperio, S.; Morganti, M.; Catoni, C.; Assandri, G.; Ramellini, S.; Rubolini, R.; Cecere, J.G.</b> (2023). Within‐colony segregation of foraging areas: from patterns to processes. <i>Oikos (Kbh.) 2023(8)</i>: e09926. <a href=\"https://dx.doi.org/10.1111/oik.09926\" target=\"_blank\">https://dx.doi.org/10.1111/oik.09926</a>","AutID":237715,"MonDate":null,"AnaDate":2023,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":353984,"RR":"<b>Fijn, R.C.; Thaxter, C.B.; Aarts, G.M; Adema, J.; Middelveld, R.P.; van Bemmelen, R.S.A.</b> (2022). 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Individual-level memory is sufficient to create spatial segregation among neighboring colonies of central-place foragers. <i>American Naturalist 198(2)</i>: E37-E52. <a href=\"https://doi.org/10.1086/715014\" target=\"_blank\">https://doi.org/10.1086/715014</a>","AutID":237715,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":335893,"RR":"<b>Chudzinska, M.; Nabe-Nielsen, J.; Smout, S.; Aarts, G.M; Brasseur, S.; Graham, I.; Thompson, P.; McConnell, B.</b> (2021). AgentSeal: Agent-based model describing movement of marine central-place foragers. <i>Ecol. Model. 440</i>: 109397. <a href=\"https://doi.org/10.1016/j.ecolmodel.2020.109397\" target=\"_blank\">https://doi.org/10.1016/j.ecolmodel.2020.109397</a>","AutID":237715,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":349452,"RR":"<b>Hintzen, N.T.; Aarts, G.M; Poos, J.J.; van der Reijden, K.J.; Rijnsdorp, A.D.</b> (2021). Quantifying habitat preference of bottom trawling gear. <i>ICES J. Mar. Sci./J. Cons. int. Explor. Mer 78(1)</i>: 172-184. <a href=\"https://dx.doi.org/10.1093/icesjms/fsaa207\" target=\"_blank\">https://dx.doi.org/10.1093/icesjms/fsaa207</a>","AutID":237715,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":347969,"RR":"<b>Hoekendijk, J.P.A.; Kellenberger, B.; Aarts, G.M; Brasseur, S.; Poiesz, S.; Tuia, D.</b> (2021). Counting using deep learning regression gives value to ecological surveys. <i>NPG Scientific Reports 11</i>: 23209. <a href=\"https://dx.doi.org/10.1038/s41598-021-02387-9\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-021-02387-9</a>","AutID":237715,"MonDate":null,"AnaDate":2021,"PeerRev":1,"outputType":"1_A1","OpenAcc":1},{"BRefID":328322,"RR":"<b>Matthiopoulos, J.; Fieberg, J.; Aarts, G.M; Barraquand, F.; Kendall, B.E.</b> (2020). Within reach? 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